
import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowercase    :   str						=			logging.get_logger(__name__)

lowercase    :   Optional[Any]						=			{
    'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
    # See all DETR models at https://huggingface.co/models?filter=detr
}




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	'detr'
	_A								=	['past_key_values']
	_A								=	{
	    'hidden_size': 'd_model',
	    'num_attention_heads': 'encoder_attention_heads',
	}







	def __init__(   self						:Tuple	,  a						:Union[str, Any]=True	,  a						:Dict=None	,  a						:Union[str, Any]=3	,  a						:Dict=1_0_0	,  a						:Union[str, Any]=6	,  a						:Optional[Any]=2_0_4_8	,  a						:Optional[Any]=8	,  a						:List[Any]=6	,  a						:List[Any]=2_0_4_8	,  a						:str=8	,  a						:Union[str, Any]=0.0	,  a						:Optional[Any]=0.0	,  a						:Union[str, Any]=True	,  a						:Dict="relu"	,  a						:Tuple=2_5_6	,  a						:List[Any]=0.1	,  a						:Optional[int]=0.0	,  a						:Optional[int]=0.0	,  a						:Union[str, Any]=0.02	,  a						:Union[str, Any]=1.0	,  a						:str=False	,  a						:int="sine"	,  a						:Dict="resnet50"	,  a						:Tuple=True	,  a						:int=False	,  a						:str=1	,  a						:Optional[int]=5	,  a						:Any=2	,  a						:str=1	,  a						:Optional[int]=1	,  a						:Tuple=5	,  a						:Any=2	,  a						:Union[str, Any]=0.1	,  **a						:Tuple	,  )						->					List[Any]:
								if backbone_config is not None and use_timm_backbone:
															raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."		)

								if not use_timm_backbone:
															if backbone_config is None:
																						logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."		)
																						__UpperCamelCase      :  Tuple       =   CONFIG_MAPPING["resnet"](out_features=["stage4"]		)
															elif isinstance(a	,  a		):
																						__UpperCamelCase      :  List[Any]       =   backbone_config.get("model_type"		)
																						__UpperCamelCase      :  List[str]       =   CONFIG_MAPPING[backbone_model_type]
																						__UpperCamelCase      :  List[Any]       =   config_class.from_dict(a		)
															# set timm attributes to None
															__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase      :  Any       =   None, None, None

								__UpperCamelCase      :  str       =   use_timm_backbone
								__UpperCamelCase      :  str       =   backbone_config
								__UpperCamelCase      :  Tuple       =   num_channels
								__UpperCamelCase      :  str       =   num_queries
								__UpperCamelCase      :  int       =   d_model
								__UpperCamelCase      :  Dict       =   encoder_ffn_dim
								__UpperCamelCase      :  Optional[Any]       =   encoder_layers
								__UpperCamelCase      :  List[Any]       =   encoder_attention_heads
								__UpperCamelCase      :  Optional[int]       =   decoder_ffn_dim
								__UpperCamelCase      :  List[Any]       =   decoder_layers
								__UpperCamelCase      :  Dict       =   decoder_attention_heads
								__UpperCamelCase      :  Optional[Any]       =   dropout
								__UpperCamelCase      :  Optional[int]       =   attention_dropout
								__UpperCamelCase      :  List[str]       =   activation_dropout
								__UpperCamelCase      :  Union[str, Any]       =   activation_function
								__UpperCamelCase      :  Any       =   init_std
								__UpperCamelCase      :  List[Any]       =   init_xavier_std
								__UpperCamelCase      :  Union[str, Any]       =   encoder_layerdrop
								__UpperCamelCase      :  Optional[Any]       =   decoder_layerdrop
								__UpperCamelCase      :  Dict       =   encoder_layers
								__UpperCamelCase      :  List[Any]       =   auxiliary_loss
								__UpperCamelCase      :  List[Any]       =   position_embedding_type
								__UpperCamelCase      :  str       =   backbone
								__UpperCamelCase      :  Optional[Any]       =   use_pretrained_backbone
								__UpperCamelCase      :  List[Any]       =   dilation
								# Hungarian matcher
								__UpperCamelCase      :  Any       =   class_cost
								__UpperCamelCase      :  Tuple       =   bbox_cost
								__UpperCamelCase      :  List[Any]       =   giou_cost
								# Loss coefficients
								__UpperCamelCase      :  str       =   mask_loss_coefficient
								__UpperCamelCase      :  str       =   dice_loss_coefficient
								__UpperCamelCase      :  Optional[int]       =   bbox_loss_coefficient
								__UpperCamelCase      :  Any       =   giou_loss_coefficient
								__UpperCamelCase      :  int       =   eos_coefficient
								super().__init__(is_encoder_decoder=a	,  **a		)







	@property
	def   _lowerCamelCase							(   self						:Optional[int]		)						->					int:
								return self.encoder_attention_heads







	@property
	def   _lowerCamelCase							(   self						:List[Any]		)						->					int:
								return self.d_model







	@classmethod
	def   _lowerCamelCase							(   cls						:str	,  a						:PretrainedConfig	,  **a						:List[str]		)						->					List[Any]:
								return cls(backbone_config=a	,  **a		)







	def   _lowerCamelCase							(   self						:List[Any]		)						->					Dict[str, any]:
								__UpperCamelCase      :  Union[str, Any]       =   copy.deepcopy(self.__dict__		)
								if output["backbone_config"] is not None:
															__UpperCamelCase      :  Union[str, Any]       =   self.backbone_config.to_dict()
								__UpperCamelCase      :  List[Any]       =   self.__class__.model_type
								return output




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	version.parse('1.11')







	@property
	def   _lowerCamelCase							(   self						:List[Any]		)						->					Mapping[str, Mapping[int, str]]:
								return OrderedDict(
								    [
								        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
								        ("pixel_mask", {0: "batch"}),
								    ]		)







	@property
	def   _lowerCamelCase							(   self						:List[Any]		)						->					float:
								return 1E-5







	@property
	def   _lowerCamelCase							(   self						:Optional[Any]		)						->					int:
								return 1_2

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase    :   Optional[int]						=			{
    'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MobileViTConfig', 'MobileViTOnnxConfig'],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[str]						=			['MobileViTFeatureExtractor']
		lowercase    :   Tuple						=			['MobileViTImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[Any]						=			[
		    'MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'MobileViTForImageClassification',
		    'MobileViTForSemanticSegmentation',
		    'MobileViTModel',
		    'MobileViTPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[Any]						=			[
		    'TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFMobileViTForImageClassification',
		    'TFMobileViTForSemanticSegmentation',
		    'TFMobileViTModel',
		    'TFMobileViTPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_mobilevit import MobileViTFeatureExtractor
				from .image_processing_mobilevit import MobileViTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mobilevit import (
				    MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MobileViTForImageClassification,
				    MobileViTForSemanticSegmentation,
				    MobileViTModel,
				    MobileViTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mobilevit import (
				    TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFMobileViTForImageClassification,
				    TFMobileViTForSemanticSegmentation,
				    TFMobileViTModel,
				    TFMobileViTPreTrainedModel,
				)


else:
		import sys

		lowercase    :   Optional[int]						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)